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Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Published by Universitas Lancang Kuning
ISSN : 20864884     EISSN : 24773255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Digital Zone journal publish by Fakultas Ilmu Komputer Universitas Lancang Kuning (Online ISSN 2477-3255 and Print ISSN 2086-4884) This journal publish two periode in a year on May and November.
Arjuna Subject : -
Articles 198 Documents
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Application of Backpropagation Neural Network in Predicting Mandatory Test Vehicle Parks Rani Asmidewi; Muhyidin, Yusuf; Minarto, Minarto
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.21030

Abstract

Backpropagation neural networks can be used in almost every aspect of human life, including the prediction of mandatory test vehicle parks. The goal of this study was to use BPNN (Backpropagation Neural Network) modeling to anticipate mandatory test vehicle parks based on the past data from the Department of Transportation at Purwakarta Regency, and to predict the results using the best model. This study makes use of mandatory test vehicle parks data from 2014 through 2023, which necessitates monthly testing. The test results show an accuracy level of 90.134% utilizing alpha 0.9, iteration number (epoch) of 10000, and MSE value 0.0064. Based on the best BPNN model into Matlab applications, the mandatory test vehicle park will be predicted from June 2023 to May 2024. The estimated value of the mandatory test vehicle park in December 2023 will be used to determine the requirement for proof of passing the periodic test in 2023 with a score of 7587
System Control of Prototype Forklift Using Android and ESP32 Based on MQTT Communication Faris, Faisal; Sulistiyowati, Indah; Hayatal Falah, Agus; Ahfas, Akhmad
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.22724

Abstract

This research develops and tests a prototype forklift robot controlled via an Internet of Things (IoT)-based system using an ESP32 microcontroller and the MQTT protocol. Designed to overcome the limitations of traditional manual forklifts, the robot enhances warehouse operations by increasing efficiency and minimizing human error. The study presents the design, testing, and evaluation of the forklift robot's movement control, mechanical wheel performance, load capacity, and wireless connectivity across various Android devices and network providers. Testing was conducted over distances ranging from 1 km to 90 km with different Android devices (e.g., Samsung, Xiaomi, OPPO, Vivo) and providers (Indosat, Axis, Telkomsel, Smartfren). The robot successfully performed a variety of movements, including forward, backward, left, right, rotation, and lifting operations. It demonstrated stable load lifting capabilities up to 300 grams, with instability observed for heavier loads, and could operate smoothly across a range of network conditions. Connectivity tests showed consistent performance and stable communication, reinforcing the robot's practical use for remote control and real-time monitoring. This research emphasizes the potential of IoT-enabled forklift robots to optimize material handling processes in warehouses, contributing to safer and more efficient industrial environments
Solution Approach to the Minimum Spanning Tree Problem in Tsukamoto Fuzzy and Fermantean Fuzzy Environments de Haas, Desli; Palembang , Citra; Rumakefing , Aziz
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.22826

Abstract

Solving Fuzzy Minimum Spanning Tree (FFMST) and Fuzzy Tsukamoto using modified Prim Algorithm for Undirected Graphs and modified Optimal Branching Algorithm for Directed Graphs in FFN environment. Since the proposed Algorithm includes FFN ranking and Arithmetic Operations, we use the improved FFN scoring function to compare the edge weights of the graphs. With the help of Numerical examples, the solution technique for the proposed FFMST model is explained. It aims to modify the Prims algorithm for oriktade graphing and the optimal result processing algorithm for re-graphing in Fuzzy Fermatean ( FFN )-miljö. They utilize the finite FFN function and the operation of fuzzy function operations to ensure victory in graphing. The fuzzy-inference process is based on the Tsukamoto method and also to get the best result from the existing catch. Numerical examples of presenters to perform the tasks of missing presenters. The results are seen in the effective Prim algorithm modifier lost Fuzzy Fermatean MST -problem for genome oriktade generator generated at minimum cost and fall with local banks. It is an optimal business growth modifier to optimize services for lenders, such as communication between financial consultants and commercial banks. This method will be effective and increase the desired parameters. Tsukamoto Fuzzy -This method includes a fuzzy-inference process to get the best answer in the minimum spanning tree problem. Kantvikter functions based on levels of capability and range functions. Theminimum spanning tree is achieved by the Prims algorithm, which may be performed with fuzzy values first.
Comparative Analysis of KNN and Neavy Bayes Algorithms in Socio-Economic Data Classification in Indonesia Buhori, Kiki; Andrianingsih
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.23337

Abstract

The global economy continues to recover as trade flows, employment, and incomes improve. However, the economic recovery is uneven across countries and business sectors. The economic recovery has also resulted in structural changes, meaning that some sectors, jobs, technologies and behaviors will not return to pre-pandemic trends. Future developments depend on local economic conditions. The economy has the most important aspect in a country where the economy makes a country capable of meeting its needs by utilizing limited resources. This study aims to compare two data mining classification algorithms, namely Naïve Bayes and K-Nearest Neighbor, in analyzing socio-economic data in Indonesia. Based on this problem, the data mining classification method is used in determining the algorithm that is suitable for predicting socio-economic data in Indonesia. The two algorithms used are K-NN and Naive Bayes. After testing the two algorithms using confusion matrix and K-Fold Cross Validation, the results obtained from the two models have an accuracy of Naïve Bayes 98.25% and K-NN 97.78% and the results of K-Fold Cross Validation Naïve Bayes 98% and K-NN 96%. Naïve Bayes is superior to K-NN in this context of socioeconomic data classification in Indonesia, especially in terms of accuracy. Although K-NN shows good consistency, Naïve Bayes provides more accurate results.
Implementation of Fuzzy Logic Sugeno on a Website-Based for Flood Monitoring and Early Detection System Pohan, Fadlyani; Mukti Qamal; Said Fadlan Anshari
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.23356

Abstract

Floods are common in Indonesia, especially in Aceh, and are caused by various factors, such as high rainfall during the rainy season, resulting in material and life losses. Developing a flood monitoring and early detection system is essential to reduce the negative impact of flooding. This research introduces the implementation of the Fuzzy Logic Sugeno method on a web-based flood monitoring and early detection system, which uses ESP32 and accurate sensors to monitor water level and water flow. Testing is done through two approaches. First, the water level and flow parameters were manipulated manually to test the system's response to changing conditions. Secondly, tests were conducted automatically until the prototype aquarium was complete to see the system's ability to detect potential flooding. The test results show that the system can automatically recognize potential flooding, take preventive actions, such as opening or closing floodgates, and provide real-time condition information through a Website.
Comparison of K-Means and K-Medoids Algorithms for Clustering Poverty Data in South Sumatra Using DBI Evaluation Akhda, M. Dandi; Tania, Ken Ditha
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.23624

Abstract

This research focuses on the implementation and comparison of the K-Means and K-Medoids algorithms that function as poverty data clustering in South Sumatra Province, the poverty data is taken from the Central Statistics Agency of Indonesia (BPS Indonesia). This research also aims to analyze the poverty level in South Sumatra Province by including additional variables such as average years of schooling and per capita expenditure in the community in each regency or city in South Sumatra Province. Data clustering is done by both algorithms and then the performance value is Evaluated using Davies Bouldin Index DBI shows that K-Means gives better results, with a lower DBI value (0.204 at K=5) while K-Medoids has a DBI value of 0.239 at K=5, which indicates more compact and separated clusters. The superiority of K-Means is due to the homogeneous and minimal outlier characteristics of the dataset, which makes the centroid approach more optimal than medoids in K-Medoids. With these results, K-Means was chosen as the best algorithm for clustering poverty data in the region. The use of the K-Means algorithm produces a pattern in clusters related to education, economic inequality, and poverty distribution in various regions in South Sumatra. This implementation provides insight into how data clustering techniques can be applied to socio-economic data to provide policy makers in a region with information about the region, especially information about poverty-stricken areas.
Evaluation of Creative Economy and Tourism Industry Trends based on LDA Analysis with BERTopic Nura Nugraha, Icha; Utami, Ema
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2024): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v15i2.23796

Abstract

Creative economy and tourism industry have a role in contributing country's foreign exchange. Efforts continue to be improved by utilizing social media. Latent Dirichlet allocation (LDA) and BERTopic topic model are used as topic models for creative economy and tourism trend analysis. The evaluation was carried out using a coherence matrix, topic distribution, similarity, and topic identification over the last five-years period. BERTopic has a higher coherence value of 0.53 compared to LDA 0.30 although the number of outlier topics dominates. The identification of the most relevant main topic trends is finance, travel, beaches and investment. These themes are interrelated in driving the growth of the creative economy and tourism, which increases local income and innovation in related sectors. BERTopic identifies hidden topics such as bitcoin cryptocurrency. In contrast, LDA provides a more even distribution of topics, revealing traditional trends such as beach tourism and travel. The evaluation offers key recommendations on creative economy and tourism policies to innovations about investment.
Clustering Of Library’s Patron Behavior Using Machine Learning Monika, Winda; Nasution, Arbi Haza; Syam, Febrizal Alfarasy; Wijesundara, Chiranthi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 1 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i1.19680

Abstract

Libraries collect a lot of important transaction data, but they rarely use this information to improve how consumers interact with them. This work tries to bridge this gap by offering a novel use of machine learning to analyze and classify library patron behavior. Customers were categorized based on their age range, checkouts, and renewals using the KMeans clustering technique. Dimensionality reduction methods like PCA and t-SNE were used to visually clarify the generated patterns. Our research revealed three different user groups: Rare Borrowers, who typically make 5.4 checkouts and 2.0 renewals; Occasional Borrowers, who typically make 20.8 checkouts and 7.1 renewals; and Frequent Borrowers, who often make 50.3 checkouts and 15.4 renewals. The clustering model performed quite well, as evidenced by its Calinski-Harabasz Index of 320.12, Davies-Bouldin Index of 0.45, and Silhouette Score of 0.62. Beyond these metrics, the study’s novelty lies in its practical implications—offering libraries a data-driven framework to tailor services, improve user satisfaction, and optimize resource allocation. This study highlights the transformative potential of machine learning in library science offering a data-driven framework for libraries to personalize services, optimize book recommendations, and enhance outreach efforts based on patron behavior. By segmenting users, libraries can better allocate resources and improve user experience. Limitation of this study lies on the data bias which may affect generalizability due to demographic differences across libraries. Additionally, KMeans clustering assumes predefined clusters, which may not fully capture nuanced behaviors.
Naïve Bayes Alpha Parameter Optimization with Ant Colony for Clinical Text Classification Taslim, Taslim; Fajrizal, Fajrizal; Handayani, Susi; Toresa, Dafwen; Lisnawita, Lisnawita
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 1 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i1.24118

Abstract

This study addresses the challenges of text classification in domain-specific Natural Language Processing (NLP) within the medical field, which differs significantly from general NLP due to the presence of complex medical jargon and informal language in clinical documents. The primary objective of this research is to develop and evaluate a cancer-related text classification model by integrating the Naïve Bayes algorithm with Laplacian smoothing and optimizing its alpha parameter using Ant Colony Optimization (ACO). Specifically, the study aims to determine whether ACO can effectively identify the optimal alpha value that enhances the classification performance of the Naïve Bayes model. Experimental results demonstrate that with an alpha value of 0.27, the proposed model achieves an accuracy of 81.05%. This indicates that the combination of ACO and Naïve Bayes significantly improves classification efficiency and accuracy. The findings contribute to more accurate interpretation of clinical cancer-related texts, supporting better-informed decision-making in medical contexts
Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection Tri Vicika, Vikha; Indra, Jamaludin; Faisal, Sutan; Hikmayanti, Hanny
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 1 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i1.26349

Abstract

Inventory management in warehouses often experiences discrepancies in recording the number of cardboard boxes due to errors during the manual recording process. To overcome this problem, a cardboard detection method was developed using the Default Mask R-CNN model and a modified model using MobileNetV3 Small. The training data was obtained from a collection of cardboard photos which then went through an annotation stage. In the cReonfiguration stage, various anchor scales were applied to determine the bounding box parameters, while the training process used Stochastic Gradient Descent (SGD). The default model is trained with the initial Mask R-CNN settings, while the custom model modifies the backbone and Feature Pyramid Network (FPN) adjustments. The test results show that the custom model has higher efficiency with a parameter count of 20,857,704 and an average FPS of 10.92. However, the accuracy level of the custom model is lower than that of the default model

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